Anumana, Inc.

Mayo Foundation for Medical Education and Research

Nference, Inc.

Ashim Prasad

Rakesh Barve

Sairam Bade

Yash Mishra

Shashi Kant

Uddeshya Upadhyay

Samir Awasthi

Shiva Verma

Melwin Babu

Murali Aravamudan

Venkataramanan Soundararajan

Tyler Wagner

Corinne Carpenter

Katherine Carlson

Paul A Friedman

Suthirth Vaidya

Itzhak Zachi Attia

Mayank Sharma

Peter A Noseworthy

Yash Gupta

Dibakar Saha

Joerg Herrmann

John Rincón-Hekking

Shayan Ghosh

Durgaprasad Dodle

An apparatus and method for training a machine learning model to augment signal data and image data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor. The memory instructs the processor to receive a signal data. The memory instructs the processor to generate a digital image, wherein the digital image comprises the signal data. The memory instructs the processor to transmit the digital image to an image processing module, wherein the image processing module produces an augmented image. The memory instructs the processor to transmit the signal data to a signal processing module, wherein the signal processing module produces the augmented image. The memory instructs the processor to train a machine learning model using the augmented image.

Apparatus and method for training a machine learning model to augment signal data and image data

A system for training multimodal self-supervised models, the system including a computing device configured to receive a multimodal dataset encode each data entry of a plurality of data entries into a single data format to create an encoded multimodal dataset including one or more encoded modalities, identify one or more embedded pairs as a function of the one or more encoded modalities, wherein each embedded pair of the one or more embedded pairs is associated with a separate patient of the plurality of patients, map the embedded pairs within a shared embedding space, pretrain an ECG machine learning model as a function of the mapping by adjusting one or more parameter values of the ECG machine learning model, receive labeled training data and train the pretrained ECG machine learning model as a function of the labeled training data.

Systems and methods for training multimodal self-supervised models

An apparatus and method for unpaired time series to time series translation is disclosed. The apparatus comprises at least a processor configured to receive an automated analysis of a time series, convert that time series from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

Apparatus and method for unpaired time series data translation

An apparatus and method for detecting a level of cardiovascular disease. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive a plurality of voltage-time data, generate at least a feature vector from the voltage-time data by at least a feature model, input the at least feature vector into a cardiovascular classification model, generate at least a disease indication in a subject using the classification model, wherein the disease indication comprises a level of myocarditis, and display the at least a disease indication.

Method and an apparatus for detecting a level of cardiovascular disease

A system for transforming electrocardiogram images for use in one or more machine learning models, the system including at least a processor configured to receive paired data including a plurality of ECG images correlated to a plurality of standardized images, wherein each ECG image of the plurality of ECG images is correlated with each standardized image of the plurality of standardized images, train an ECG transformation model as a function of the paired data, wherein training the transformation model includes adjusting one or more parameter values of the ECG transformation model as a function of a comparison between at least one predicted standardized image and at least one standardized image of the plurality of standardized images, receive non-conforming data, generate standardized data as a function of the ECG transformation model and the non-conforming data and train an ECG machine learning model as a function of the standardized data.

Systems and methods for transforming electrocardiogram images for use in one or more machine learning models

A system for training machine learning models with unlabeled electrocardiogram signals, the system including a memory containing instructions configurating a processor to receive a plurality of electrocardiogram (ECG) data in a textual format, create one or more overlapping temporal patches from the plurality of ECG data, mask at least one temporal patch from the one or more overlapping temporal patches, pretrain an ECG machine learning model to predict the at least one masked temporal patch from the one or more overlapping temporal patches, adjust one or more parameter values of the ECG machine learning model as a function of the at least one predicted masked temporal patch and the at least one masked temporal patch and train the ECG machine learning model as a function of the one or more parameter values and a labeled set of ECG training data.

Systems and methods for training machine learning models using unlabeled electrocardiogram data

An apparatus and method for generating a quality diagnostic of analog ECG (electrocardiogram) data. The apparatus includes at least a processor and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to receive ECG data, extract a plurality of ECG parameters from the ECG data, convert the ECG data to one or more digitized ECG signals, validate the digitized ECG signals, wherein validating includes classifying the plurality of digitized ECG signals to a plurality of preliminary ECG parameters and determining an accuracy status of the extracted plurality of ECG parameters by comparing the plurality of preliminary ECG parameters to the extracted plurality of ECG parameters, and generate a quality diagnostic of the ECG data based on validation of the digitized ECG signals.

Apparatus and method for generating a quality diagnostic of ECG (electrocardiogram) data

Disclosed systems, methods, and computer readable media can diagnose a health condition based on patient time series data. For example, a method for diagnosing a health condition based on patient time series data includes identifying a training set of health records comprising a first set of patient time series data, training a neural network using the training set of health records, and executing the trained neural network model to diagnose a health condition based on a second set of patient time series data. In further examples, the first set of patient time series data and the second set of patient time series data can each comprise electrocardiogram data and the health condition can comprise pulmonary hypertension.

Systems and methods for diagnosing a health condition based on patient time series data

An apparatus for standardization of electrocardiogram signal images, the apparatus having an imaging device, at least a processor and a memory communicatively connected to the at least a processor, the memory containing instructions configuring the at least a processor to receive an overlay image from the imaging device, identify a captured fixed background image and a captured primary image within the overlay image, wherein the captured primary image includes a plurality of electrocardiogram signals, compare the captured fixed background image to one or more image quality thresholds, determine an image quality score of the primary image as a function of the captured fixed background image, and output one or more image modification datum as a function of the image quality score.

Systems and methods for standardization of electrocardiogram signal images

Systems and methods for populating a structure database including accessing an image representation of a data table comprising one or more cells arranged in rows and columns; providing the image representation as an input to a neural network model; executing the neural network model to identify a location of the first content object in the image representation; identifying a location of the first cell based on the location of the first content object; determining that the first cell belongs to the first row and the first column based on the location of the first cell and the first content object in relation to a plurality of content objects; associating the first content object with one or more categorical identifiers; and populating a structured database with the first content object and the one or more categorical identifiers.

Growing community of inventors

Bengaluru, India

Ashim Prasad